Top AI & Machine Learning Jobs

Designs, develops, and deploys enterprise Generative AI solutions using LLMs, semantic search, and AI orchestration. Builds monitoring systems, improves AI architecture, supports prompt engineering and evaluation, reviews code, and mentors engineers. Collaborates with product leaders and stakeholders to deliver secure, scalable, customer-facing AI applications. Requires Azure OpenAI, Azure AI Search, Semantic Kernel, DevOps, CI/CD, testing, and advanced Python expertise.

Conduct a semester-long applied AI research project with an AfterQuery mentor in areas such as data quality, post-training, agent and foundation-model evaluation, or public benchmarks. Responsibilities include formulating research questions, designing experiments, developing datasets and evaluation methods, analyzing results, documenting reproducible work, and co-authoring a publication. Scholars receive mentorship, engineering support, annotation resources, and compute access.

Conduct independent AI research for predictive toxicology and drug safety. Develop novel deep learning architectures, multimodal models, representations, uncertainty methods, and mechanistic reasoning approaches for heterogeneous biological and chemical data. Design rigorous experiments, benchmarks, ablations, and generalization evaluations while collaborating with engineering and scientific teams to translate validated research into scalable modeling systems. Contribute to scientific strategy, publications, collaborations, and regulatory validation.

Leads Nuro’s Software Operations organization, overseeing AI training and evaluation data, autonomy regression analysis, release validation, operational KPIs, and root-cause investigations. Manages large-scale data-labeling vendors, including quality, throughput, SLAs, budgets, and pricing. Partners with engineering, machine learning, data science, simulation, and product teams to build evaluation datasets, improve autonomy performance, and scale operational processes and teams.

Leads the strategy, architecture, development, deployment, monitoring, and optimization of an agentic AI platform. Transforms products, processes, and workflows into scalable AI-enabled capabilities; establishes standards for performance, security, governance, and reliability; evaluates models and orchestration frameworks; partners cross-functionally on technical roadmaps; and builds and mentors high-performing engineering teams.

Build and deploy production generative AI and machine learning systems for utility clients. Own engagements end to end, including discovery, architecture, retrieval, model selection, evaluation, deployment, monitoring, operator-facing applications, and post-go-live support. Integrate with client data and business systems, establish AI safety and governance controls, manage scope and risk, and communicate with technical teams and executives. Contribute reusable accelerators, evaluation frameworks, and reference architectures across client engagements.

Owns enterprise AI platforms, including tenant administration, LLM gateways, MCP integrations, agentic workflows, cloud AI workloads, security, governance, cost monitoring, and adoption. Builds integrations and internal tooling primarily in Python and TypeScript across AWS and GCP. Partners with cybersecurity, AI governance, finance, and executives to enforce policies, operate reliable systems, and report on usage, performance, and spend.

Build and improve intelligent document-processing automations for complex utility bills. Analyze document variations, configure automation logic, troubleshoot issues, and collaborate with product engineering and operations teams. Develop subject-matter expertise in utility billing and data flows while applying document modeling, validation logic, AI tools, and related technologies to improve speed and accuracy.

Owns statistical, Bayesian, and machine learning solutions from development through production. Builds models for lead scoring, retention, forecasting, and segmentation; applies LLMs to unstructured data; develops Databricks and Azure pipelines; creates React and FastAPI applications; and communicates analytical findings to business stakeholders across sports and entertainment functions.

Supports enterprise AI adoption by developing strategies, roadmaps, business cases, and implementation plans. Facilitates workshops, identifies and prioritizes AI use cases, delivers training and enablement programs, builds AI champion networks, researches emerging technologies, and tracks adoption outcomes. The role also applies change management, human-centered design, responsible AI practices, and generative AI expertise to translate capabilities into practical business recommendations.